Instructions to use Hcompany/Holo4-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hcompany/Holo4-35B-A3B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Hcompany/Holo4-35B-A3B-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Hcompany/Holo4-35B-A3B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Hcompany/Holo4-35B-A3B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hcompany/Holo4-35B-A3B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Hcompany/Holo4-35B-A3B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hcompany/Holo4-35B-A3B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Hcompany/Holo4-35B-A3B-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Hcompany/Holo4-35B-A3B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Hcompany/Holo4-35B-A3B-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Hcompany/Holo4-35B-A3B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hcompany/Holo4-35B-A3B-GGUF:F16

Use Docker

docker model run hf.co/Hcompany/Holo4-35B-A3B-GGUF:F16

LM Studio
Jan

vLLM

How to use Hcompany/Holo4-35B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hcompany/Holo4-35B-A3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hcompany/Holo4-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Hcompany/Holo4-35B-A3B-GGUF:F16

SGLang

How to use Hcompany/Holo4-35B-A3B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Hcompany/Holo4-35B-A3B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hcompany/Holo4-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Hcompany/Holo4-35B-A3B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hcompany/Holo4-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Hcompany/Holo4-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/Hcompany/Holo4-35B-A3B-GGUF:F16
```
Unsloth Desktop

How to use Hcompany/Holo4-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hcompany/Holo4-35B-A3B-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Hcompany/Holo4-35B-A3B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Hcompany/Holo4-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/Hcompany/Holo4-35B-A3B-GGUF:F16
```

Lemonade

How to use Hcompany/Holo4-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hcompany/Holo4-35B-A3B-GGUF:F16

Run and chat with the model

lemonade run user.Holo4-35B-A3B-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use Hcompany/Holo4-35B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hcompany/Holo4-35B-A3B-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Hcompany/Holo4-35B-A3B-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Hcompany/Holo4-35B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hcompany/Holo4-35B-A3B-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Hcompany/Holo4-35B-A3B-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Holo4-35B-A3B-GGUF

Holo4 family:

Holo4-27B: Qwen3.8 dense architecture. BF16, FP8, NVFP4, and Q4 GGUF.

Holo4-35B-A3B: Qwen3.6 MoE architecture. BF16, FP8, NVFP4, and Q4 GGUF [current].

Holotron4-30B-A3B: NemotronH Nano Omni architecture. BF16 and FP8.

Model summary

Holo4-35B-A3B-GGUF is a vision-language model (VLM) for Computer Use, built on Qwen3.6-35B-A3B and developed by H Company. Used with the hai-agents harness, it can send screenshots and tool results to the model, then execute its requested clicks, typing, code, and tool calls.

Specification	Value
Model ID	`Hcompany/Holo4-35B-A3B-GGUF`
Architecture	Qwen3.6 MoE
Checkpoint format	Q4_K_M GGUF
Maximum context length in config	262,144 tokens

This demo shows Holo4-27B using FreeCAD to build a replica of the Eiffel Tower. More examples in the blog post.

Prompt

Build a 3D Eiffel Tower in FreeCAD at a scale of 1 mm per metre. Center it on the origin and align it with the X and Y axes. Its plan must stay square at every height. The distance from the center to each corner is 62.5 mm at ground level, 32.5 mm at height 57, 17.5 mm at height 115, and 9.35 mm at height 276. Connect these widths with a smooth curve that narrows quickly near the base and more slowly near the top.

Make four separate, identical square legs, one in each quadrant. Their outer corners follow that curve, and each leg narrows from 14 mm across at ground level to 4 mm at height 276. Leave the space between the legs open. Add centered square platforms measuring 72 mm by 72 mm by 4 mm at height 57, 40 mm by 40 mm by 3 mm at height 115, and 22 mm by 22 mm by 3 mm at height 276. Add a square mast from height 276 to 324, tapering from 8 mm across to 2 mm across. Make every component a closed solid with nonzero volume, without filling the space between the legs.

Usage

The harness sends screenshots and tool results to Holo4, executes the model's requested actions, and sends the results back. It can give the model access to application tools and code execution.

Refer to the documentation for more details about:

Performance

Holo4 models improve significantly over their Qwen base models. On OSWorld, Holo4-27B scores 85.2% at $0.08 per task. We also evaluate them on Agentic Task Factory, a set of held-out business workflows across web, desktop, and MCP tools.

Benchmark results

Read the blog post for more tables about the benchmark results.

Pareto plots

These plots compare benchmark scores with cost per task. On OSWorld 2.0, Holo4-27B scores 61.7% at $1.22 per task, and Holo4-35B-A3B scores 30.9% at $0.61 per task.

On AutomationBench, Holo4-27B scores 45.4% at $0.05 per task, and Holo4-35B-A3B scores 34.5% at $0.02 per task.

Open-source evaluation traces

For transparency, we share all agent trajectories in the open-source dataset at Hcompany/trajectories.

Training

License

The model weights are available under the Apache License 2.0. This model is built on Qwen3.6-35B-A3B, which Alibaba Cloud also releases under the Apache License 2.0.